MCI_TO_AD_Progresion_V2.Rmd

---
title: "TADPOLE MCI to AD Conversion"
output: html_notebook
editor_options: 
  chunk_output_type: console
---

```{r}
library("FRESA.CAD")

```

# Predict MCI to AD progresion

```{r}
resamplingNumber = 5
months <- c(0,3,6,12,18,24,30,36,42,48,54,60,66,72,78,84,90,96,102,108,114,120)
basechage <- c(1,3:length(months))
months[basechage]
oneyearchage <- c(6:length(months))
months[oneyearchage]
Twoyearchage <- c(8:length(months))
months[Twoyearchage]
Threeyearchage <- c(10:length(months))
months[Threeyearchage]
Fouryearchage <- c(12:length(months))
months[Fouryearchage]
Fiveyearchage <- c(14:length(months))
months[Fiveyearchage]

#load(file="allAdusted.RDATA")
load(file="allAdustedZrank.RDATA")
allAdusted <- allAdustedZrank
predictors <- c("AGE","PTGENDER",colnames(allAdusted)[-c(1:22)])
cpredictors <- predictors

allAdusted <- allAdusted[order(allAdusted$Years_bl),]
allAdusted <- allAdusted[order(allAdusted$PTID),]

pdis <- allAdusted$PTID
lastTimepointSet <- allAdusted[c(pdis[1:(length(pdis)-1)] != pdis[-1],TRUE),]
rownames(lastTimepointSet) <- lastTimepointSet$PTID
hist(lastTimepointSet$Years_bl)

BaseTimepointSet <- allAdusted[c(TRUE,pdis[-1] != pdis[1:(length(pdis)-1)]),]
rownames(BaseTimepointSet) <- BaseTimepointSet$PTID


deltaFeaturepredictors <- predictors[regexpr('_bl', predictors) < 0][-(c(1:2))]

TimePointsSubset <- list();
Orderbytimepoint <- NULL
m <- 0
i <- 1;
for (m in months)
{
  TimePointsSubset[[i]] <- subset(allAdusted,M == m)
  rownames(TimePointsSubset[[i]]) <- TimePointsSubset[[i]]$PTID
  TimePointsSubset[[i]]$Year_bl_LastVisit <- lastTimepointSet[TimePointsSubset[[i]]$PTID,"Years_bl"]
  TimePointsSubset[[i]]$Last_DX <- lastTimepointSet[TimePointsSubset[[i]]$PTID,"DX"]
  TimePointsSubset[[i]]$TimeToLastVisit <- TimePointsSubset[[i]]$Year_bl_LastVisit - TimePointsSubset[[i]]$Years_bl
  deltaObservations <- TimePointsSubset[[i]][,deltaFeaturepredictors] - BaseTimepointSet[rownames(TimePointsSubset[[i]]),deltaFeaturepredictors]
  colnames(deltaObservations) <- paste("Delta",colnames(deltaObservations),sep="_")
  TimePointsSubset[[i]] <- cbind(TimePointsSubset[[i]],deltaObservations)
  TimePointsSubset[[i]] <- TimePointsSubset[[i]][complete.cases(TimePointsSubset[[i]]),]
  Orderbytimepoint <- rbind(Orderbytimepoint,TimePointsSubset[[i]])
  i <- i + 1
}

allAdusted <- Orderbytimepoint
Orderbytimepoint <- NULL
predictors <- c(predictors,colnames(deltaObservations))

ADNITRAIN <- allAdusted[allAdusted$D1==1,]
ADNITEST <- allAdusted[allAdusted$D2==1,]


#deltaPredictors <- allAdusted[,deltaFeaturepredictors]
#totsub <- nrow(deltaPredictors)
#deltaPredictors[2:totsub,] <- deltaPredictors[2:totsub,] - deltaPredictors[1:(totsub-1),]
#settozero <- c(TRUE,pdis[2:totsub] != pdis[1:(totsub-1)])
#deltaPredictors[settozero,] <- numeric(ncol(deltaPredictors))
#rownames(deltaPredictors) <- rownames(allAdusted)
#colnames(deltaPredictors) <- paste("Delta",colnames(deltaPredictors),sep="")
#allAdusted <- cbind(allAdusted,deltaPredictors)


```


## Get All the MCI subjects that progressed

```{r}
table(allAdusted$DX)
MCISubset <- subset(allAdusted,(DX_bl == "LMCI" | DX_bl == "EMCI") & DX == "MCI")


subsetMCIADConversion <-  as.data.frame(subset(allAdusted,DX == "MCI to Dementia"))
pidss <- subsetMCIADConversion$PTID
tpids <- table(pidss)
removeTp <- tpids[pidss] == 1
sum(removeTp == FALSE)

subsetMCIADConversion <- subsetMCIADConversion[removeTp,]

rownames(subsetMCIADConversion) <- subsetMCIADConversion$PTID

```

### Subset by time points

```{r}

MCItoADorderbytimepoint <- NULL
for (m in months)
{
  TimePointsMCISubset <- subset(MCISubset,M == m)
  rownames(TimePointsMCISubset) <-  TimePointsMCISubset$PTID
  TimePointsMCISubset$TimeToEvent <- subsetMCIADConversion[TimePointsMCISubset$PTID,"Years_bl"] - TimePointsMCISubset$Years_bl
  MCItoADorderbytimepoint <- rbind(MCItoADorderbytimepoint,TimePointsMCISubset)
}

controlMCIToADset <- MCItoADorderbytimepoint[is.na(MCItoADorderbytimepoint$TimeToEvent),]
controlMCIToADset <- subset(controlMCIToADset,Year_bl_LastVisit > 4)
hist(controlMCIToADset$TimeToLastVisit)
controlMCIToADset$TimeToEvent <- controlMCIToADset$TimeToLastVisit

caseMCIToADset <- MCItoADorderbytimepoint[!is.na(MCItoADorderbytimepoint$TimeToEvent),]
caseMCIToADset <- subset(caseMCIToADset,TimeToEvent > 0 & TimeToEvent <= 5)
hist(caseMCIToADset$TimeToEvent)


```
## Modeling Set
```{r}

controlMCIToADset$class <- 0
caseMCIToADset$class <- 1
MCI_to_AD_set <- rbind(controlMCIToADset,caseMCIToADset)
MCI_to_AD_set$TimeToLastVisit <- NULL

MCI_to_AD_TrainSet <- MCI_to_AD_set[MCI_to_AD_set$D1==1,]

table(MCI_to_AD_TrainSet$class)



```
## Modeling


```{r}
table(MCI_to_AD_TrainSet$VISCODE)

MCI_to_ADSets <- list();
MCI_TO_AD_Model <- list();
n=1

for (n in 1:resamplingNumber)
{
  randomnumber <- sample(1:nrow(MCI_to_AD_TrainSet),nrow(MCI_to_AD_TrainSet))
  MCI_to_AD_RandomSet <- MCI_to_AD_TrainSet[randomnumber,]
  MCI_to_AD_RandomSet <- MCI_to_AD_RandomSet[order(MCI_to_AD_RandomSet$PTID),]
  ptID <- MCI_to_AD_RandomSet$PTID
  set1 <- MCI_to_AD_RandomSet[c(ptID[1:length(ptID)-1] != ptID[-1],TRUE),]
  rownames(set1) <- set1$PTID
  MCI_to_ADSets[[n]] <- set1[,c("class",predictors)]
  MCI_to_ADSets[[n]] <- MCI_to_ADSets[[n]][complete.cases(MCI_to_ADSets[[n]]),]
  MCI_TO_AD_Model[[n]] <- BSWiMS.model(class ~ 1,MCI_to_ADSets[[n]],NumberofRepeats = 5)
}
save(MCI_TO_AD_Model,file="MCIMOdels.RDATA")


pred <- numeric(nrow(MCI_to_AD_TrainSet))
for (n in 1:resamplingNumber)
{
  sm <- summary(MCI_TO_AD_Model[[n]])
  print(sm$coefficients)
  pred <- pred+predict(MCI_TO_AD_Model[[n]],MCI_to_AD_TrainSet)/resamplingNumber
}


pb <- predictionStats_binary(cbind(MCI_to_AD_TrainSet$class,pred),plotname = "BSWiMS MCI to AD")

```

### Time to Event

```{r}

MCI_to_ADSets <- list();
MCI_TO_AD_TimeModel <- list();
n=1

#caseTrainnoBL <- subset(MCI_to_AD_TrainSet,class==1 & VISCODE != "bl")
caseTrain <- subset(MCI_to_AD_TrainSet,class==1 )

for (n in 1:resamplingNumber)
{
  randomnumber <- sample(1:nrow(caseTrain),nrow(caseTrain))
  MCI_to_AD_RandomSet <- caseTrain[randomnumber,]
  MCI_to_AD_RandomSet <- MCI_to_AD_RandomSet[order(MCI_to_AD_RandomSet$PTID),]
  ptID <- MCI_to_AD_RandomSet$PTID
  set1 <- MCI_to_AD_RandomSet[c(ptID[1:length(ptID)-1] != ptID[-1],TRUE),]
  rownames(set1) <- set1$PTID
  MCI_to_ADSets[[n]] <- set1[,c("TimeToEvent",predictors)]
  MCI_to_ADSets[[n]]$TimeToEvent <- log(MCI_to_ADSets[[n]]$TimeToEvent)
  MCI_TO_AD_TimeModel[[n]] <- BSWiMS.model(TimeToEvent ~ 1,MCI_to_ADSets[[n]],NumberofRepeats = 5)
}

save(MCI_TO_AD_TimeModel,file="MCI_TO_AD_TimeModel.RDATA")

MCI_to_AD_TestCaseSet <- MCI_to_AD_set[MCI_to_AD_set$D2==1 & MCI_to_AD_set$class==1,]

pred <- numeric(nrow(MCI_to_AD_TestCaseSet))
for (n in 1:resamplingNumber)
{
  sm <- summary(MCI_TO_AD_TimeModel[[n]])
  print(sm$coefficients)
  pred <- pred+predict(MCI_TO_AD_TimeModel[[n]],MCI_to_AD_TestCaseSet)
}

pred <- pred/resamplingNumber

ps <- predictionStats_regression(cbind(MCI_to_AD_TestCaseSet$TimeToEvent,exp(pred)),plotname = "BSWiMS MCI to AD Time")
ps$spearmanci

```



## Get All the NC subjects that progressed

```{r}
table(allAdusted$DX,allAdusted$DX_bl)
NCSubset <- subset(allAdusted,(DX_bl == "CN" | DX_bl == "SMC") & DX == "NL")


subsetNCADConversion <-  as.data.frame(subset(allAdusted,DX == "NL to Dementia" | DX == "NL to MCI"))
pidss <- subsetNCADConversion$PTID
tpids <- table(pidss)
removeTp <- tpids[pidss] == 1
sum(removeTp == FALSE)

subsetNCConvConversion <- subsetNCADConversion[removeTp,]

rownames(subsetNCConvConversion) <- subsetNCConvConversion$PTID

```

### Subset by time points

```{r}

NCConvorderbytimepoint <- NULL
for (m in months)
{
  TimePointsNCSubset <- subset(NCSubset,M == m)
  rownames(TimePointsNCSubset) <-  TimePointsNCSubset$PTID
  TimePointsNCSubset$TimeToEvent <- subsetNCConvConversion[TimePointsNCSubset$PTID,"Years_bl"] - TimePointsNCSubset$Years_bl
  NCConvorderbytimepoint <- rbind(NCConvorderbytimepoint,TimePointsNCSubset)
}

controlNCConvset <- NCConvorderbytimepoint[is.na(NCConvorderbytimepoint$TimeToEvent),]
controlNCConvset <- subset(controlNCConvset, Year_bl_LastVisit >= 4)
hist(controlNCConvset$TimeToLastVisit)
controlNCConvset$TimeToEvent <- 5*controlNCConvset$TimeToLastVisit
caseNCConvset <- NCConvorderbytimepoint[!is.na(NCConvorderbytimepoint$TimeToEvent),]
caseNCConvset <- subset(caseNCConvset,TimeToEvent > 0 & TimeToEvent <= 5)
hist(caseNCConvset$TimeToEvent)


```
## Modeling Set
```{r}

controlNCConvset$class <- 0
caseNCConvset$class <- 1
NCConv_set <- rbind(controlNCConvset,caseNCConvset)

NCConv_TrainSet <- NCConv_set[NCConv_set$D1==1,]

table(NCConv_TrainSet$class)

```
## Modeling


```{r}
table(NCConv_TrainSet$VISCODE)

NCConvSets <- list();
NCConv_Model <- list();
n=1


for (n in 1:resamplingNumber)
{
  randomnumber <- sample(1:nrow(NCConv_TrainSet),nrow(NCConv_TrainSet))
  NCConv_RandomSet <- NCConv_TrainSet[randomnumber,]
  NCConv_RandomSet <- NCConv_RandomSet[order(NCConv_RandomSet$PTID),]
  ptID <- NCConv_RandomSet$PTID
  set1 <- NCConv_RandomSet[c(ptID[1:length(ptID)-1] != ptID[-1],TRUE),]
  rownames(set1) <- set1$PTID
  NCConvSets[[n]] <- set1[,c("class",predictors)]
  NCConvSets[[n]] <- NCConvSets[[n]][complete.cases(NCConvSets[[n]]),]
  NCConv_Model[[n]] <- BSWiMS.model(class ~ 1,NCConvSets[[n]],NumberofRepeats = 5)
}
save(NCConv_Model,file="NCConv_Model.RDATA")

pred <- numeric(nrow(NCConv_TrainSet))
for (n in 1:resamplingNumber)
{
  sm <- summary(NCConv_Model[[n]])
  print(sm$coefficients)
  pred <- pred+predict(NCConv_Model[[n]],NCConv_TrainSet)/resamplingNumber
}

pb <- predictionStats_binary(cbind(NCConv_TrainSet$class,pred),plotname = "BSWiMS MCI to AD")

```

### Time to Event

```{r}

NL_TO_OTHER_Sets <- list();
NL_TO_OTHER_TimeModel <- list();
n=1

#caseTrainnoBL <- subset(NL_TO_OTHER_TrainSet,class==1 & VISCODE != "bl")
caseTrain <- subset(NCConv_TrainSet,class==1 )

for (n in 1:resamplingNumber)
{
  randomnumber <- sample(1:nrow(caseTrain),nrow(caseTrain))
  NL_TO_OTHER_RandomSet <- caseTrain[randomnumber,]
  NL_TO_OTHER_RandomSet <- NL_TO_OTHER_RandomSet[order(NL_TO_OTHER_RandomSet$PTID),]
  ptID <- NL_TO_OTHER_RandomSet$PTID
  set1 <- NL_TO_OTHER_RandomSet[c(ptID[1:length(ptID)-1] != ptID[-1],TRUE),]
  rownames(set1) <- set1$PTID
  NL_TO_OTHER_Sets[[n]] <- set1[,c("TimeToEvent",predictors)]
  NL_TO_OTHER_Sets[[n]]$TimeToEvent <- log(NL_TO_OTHER_Sets[[n]]$TimeToEvent)
  NL_TO_OTHER_TimeModel[[n]] <- BSWiMS.model(TimeToEvent ~ 1,NL_TO_OTHER_Sets[[n]],NumberofRepeats = 5)
}
save(NL_TO_OTHER_TimeModel,file="NL_TO_OTHER_TimeModel.RDATA")

NL_TO_OTHER_TestCaseSet <- NCConv_TrainSet[NCConv_TrainSet$D2==1 & NCConv_TrainSet$class==1,]

pred <- numeric(nrow(NL_TO_OTHER_TestCaseSet))
for (n in 1:resamplingNumber)
{
  sm <- summary(NL_TO_OTHER_TimeModel[[n]])
  print(sm$coefficients)
  pred <- pred+predict(NL_TO_OTHER_TimeModel[[n]],NL_TO_OTHER_TestCaseSet)
}

pred <- pred/resamplingNumber

ps <- predictionStats_regression(cbind(NL_TO_OTHER_TestCaseSet$TimeToEvent,exp(pred)),plotname = "BSWiMS MCI to AD Time")
ps$spearmanci

```



## CrossSectional Modeling

## Modeling Set
```{r}
table(ADNITRAIN$DX,ADNITRAIN$DX_bl)

class <- 
  2*(ADNITRAIN$DX == "Dementia" | ADNITRAIN$DX == "MCI to Dementia" | ADNITRAIN$DX == "NL to Dementia") +
  1*(ADNITRAIN$DX == "Dementia to MCI" | ADNITRAIN$DX == "MCI" | ADNITRAIN$DX == "NL to MCI")

ADNITRAIN$class <- class

class <- 
  2*(ADNITEST$DX == "Dementia" | ADNITEST$DX == "MCI to Dementia" | ADNITEST$DX == "NL to Dementia") +
  1*(ADNITEST$DX == "Dementia to MCI" | ADNITEST$DX == "MCI" | ADNITEST$DX == "NL to MCI")


ADNITEST$class <- class

```
## Baseline Modeling


```{r}

AllADNISets <- list();
AllADNI_Model <- list();
n=1

resamplingNumber = 5


for (n in 1:resamplingNumber)
{
  randomnumber <- sample(1:nrow(ADNITRAIN),nrow(ADNITRAIN))
  AllADNI_RandomSet <- ADNITRAIN[randomnumber,]
  AllADNI_RandomSet <- AllADNI_RandomSet[order(AllADNI_RandomSet$PTID),]
  ptID <- AllADNI_RandomSet$PTID
  set1 <- AllADNI_RandomSet[c(ptID[1:length(ptID)-1] != ptID[-1],TRUE),]
  rownames(set1) <- set1$PTID
  AllADNISets[[n]] <- set1[,c("class",cpredictors)]
  AllADNISets[[n]] <- AllADNISets[[n]][complete.cases(AllADNISets[[n]]),]
  AllADNISets[[n]]$class <- as.factor(AllADNISets[[n]]$class)
  AllADNI_Model[[n]] <- filteredFit(class ~ .,
                                    AllADNISets[[n]],
                                    fitmethod=e1071::svm,
                                    filtermethod.control=list(pvalue=0.05,limit=150))
#  AllADNI_Model[[n]] <- filteredFit(class ~ .,
#                                    AllADNISets[[n]],
#                                    fitmethod=NAIVE_BAYES,
#                                    filtermethod.control=list(pvalue=0.01,limit=25),usekernel = TRUE)
#  AllADNI_Model[[n]] <- BSWiMS.model(class ~ 1,AllADNISets[[n]],NumberofRepeats = 5)
#  AllADNI_Model[[n]] <- e1071::svm(as.formula(class ~ .),AllADNISets[[n]][,c(1:20)])
}

#summary(set1[,c("class",cpredictors[1:20])])

#save(AllADNI_Model,file="OrdinalMOdels.RDATA")
#load(file="OrdinalMOdels.RDATA")

pred <- predict(AllADNI_Model[[1]],ADNITEST)
table(pred,ADNITEST$class)

#pred <- predict(AllADNI_Model[[1]]$oridinalModels,ADNITEST)

for (n in 2:resamplingNumber)
{
#  pred <- pred+predict(AllADNI_Model[[n]]$oridinalModels,ADNITEST)
  pred <- pred+predict(AllADNI_Model[[n]],ADNITEST)
}
pred <- pred/resamplingNumber

#pred <- as.data.frame(pred)
#table(pred[,1])

#pb <- predictionStats_ordinal(cbind(ADNITEST$class,as.integer(pred[,1]+0.5)),plotname = "ADNI Classificaiton")
pb <- predictionStats_ordinal(cbind(ADNITEST$class,as.integer(pred+0.5)),plotname = "ADNI Classificaiton")

pb$Kendall
pb$Kapp
pb$class95ci
  
table(ADNITEST$class,as.integer(pred+0.5))

```


## One Year Modeling

```{r}

resamplingNumber = 5

AdniFive <- NULL
idx = 1;
for (idx in 1:length(Fiveyearchage))
{
  baseFive <- TimePointsSubset[[basechage[idx]]]
  baseFive$DX <- rep(NA,nrow(baseFive))
  baseFive[rownames(TimePointsSubset[[Fiveyearchage[idx]]]),"DX"] <- TimePointsSubset[[Fiveyearchage[idx]]]$DX
  baseFive <- baseFive[!is.na(baseFive$DX),]
  AdniFive <- rbind(AdniFive,baseFive)
}
table(ADNITRAIN$DX,ADNITRAIN$DX_bl)

ADNITRAIN <- subset(AdniFive,D1 == 1)
ADNITEST <- subset(AdniFive,D2 == 1)
class <- 
  2*(ADNITRAIN$DX == "Dementia" | ADNITRAIN$DX == "MCI to Dementia" | ADNITRAIN$DX == "NL to Dementia") +
  1*(ADNITRAIN$DX == "Dementia to MCI" | ADNITRAIN$DX == "MCI" | ADNITRAIN$DX == "NL to MCI")

ADNITRAIN$class <- class

class <- 
  2*(ADNITEST$DX == "Dementia" | ADNITEST$DX == "MCI to Dementia" | ADNITEST$DX == "NL to Dementia") +
  1*(ADNITEST$DX == "Dementia to MCI" | ADNITEST$DX == "MCI" | ADNITEST$DX == "NL to MCI")

ADNITEST$class <- class

```

## The Five Year prediction Models

```{r}

for (n in 1:resamplingNumber)
{
  randomnumber <- sample(1:nrow(ADNITRAIN),nrow(ADNITRAIN))
  AllADNI_RandomSet <- ADNITRAIN[randomnumber,]
  AllADNI_RandomSet <- AllADNI_RandomSet[order(AllADNI_RandomSet$PTID),]
  ptID <- AllADNI_RandomSet$PTID
  set1 <- AllADNI_RandomSet[c(ptID[1:length(ptID)-1] != ptID[-1],TRUE),]
  rownames(set1) <- set1$PTID
  AllADNISets[[n]] <- set1[,c("class",cpredictors)]
  AllADNISets[[n]] <- AllADNISets[[n]][complete.cases(AllADNISets[[n]]),]
  AllADNISets[[n]]$class <- as.factor(AllADNISets[[n]]$class)
  AllADNI_Model[[n]] <- filteredFit(class ~ .,
                                    AllADNISets[[n]],
                                    fitmethod=e1071::svm,
                                    filtermethod.control=list(pvalue=0.05,limit=150))
}

pred <- predict(AllADNI_Model[[1]],ADNITEST)

for (n in 2:resamplingNumber)
{
  pred <- pred+predict(AllADNI_Model[[n]],ADNITEST)
}
pred <- pred/resamplingNumber

pb <- predictionStats_ordinal(cbind(ADNITEST$class,as.integer(pred+0.5)),plotname = "ADNI Classificaiton")

pb$class95ci
  
table(ADNITEST$class,as.integer(pred+0.5))

```



## Five Year Modeling

```{r}

resamplingNumber = 5

AdniFive <- NULL
idx = 1;
for (idx in 1:length(Fiveyearchage))
{
  baseFive <- TimePointsSubset[[basechage[idx]]]
  baseFive$DX <- rep(NA,nrow(baseFive))
  baseFive[rownames(TimePointsSubset[[Fiveyearchage[idx]]]),"DX"] <- TimePointsSubset[[Fiveyearchage[idx]]]$DX
  baseFive <- baseFive[!is.na(baseFive$DX),]
  AdniFive <- rbind(AdniFive,baseFive)
}
table(ADNITRAIN$DX,ADNITRAIN$DX_bl)

ADNITRAIN <- subset(AdniFive,D1 == 1)
ADNITEST <- subset(AdniFive,D2 == 1)
class <- 
  2*(ADNITRAIN$DX == "Dementia" | ADNITRAIN$DX == "MCI to Dementia" | ADNITRAIN$DX == "NL to Dementia") +
  1*(ADNITRAIN$DX == "Dementia to MCI" | ADNITRAIN$DX == "MCI" | ADNITRAIN$DX == "NL to MCI")

ADNITRAIN$class <- class

class <- 
  2*(ADNITEST$DX == "Dementia" | ADNITEST$DX == "MCI to Dementia" | ADNITEST$DX == "NL to Dementia") +
  1*(ADNITEST$DX == "Dementia to MCI" | ADNITEST$DX == "MCI" | ADNITEST$DX == "NL to MCI")

ADNITEST$class <- class

```


```{r}
AllADNISets <- list();
AllADNI_Model <- list();
n=1
for (n in 1:resamplingNumber)
{
  randomnumber <- sample(1:nrow(ADNITRAIN),nrow(ADNITRAIN))
  AllADNI_RandomSet <- ADNITRAIN[randomnumber,]
  AllADNI_RandomSet <- AllADNI_RandomSet[order(AllADNI_RandomSet$PTID),]
  ptID <- AllADNI_RandomSet$PTID
  set1 <- AllADNI_RandomSet[c(ptID[1:length(ptID)-1] != ptID[-1],TRUE),]
  rownames(set1) <- set1$PTID
  AllADNISets[[n]] <- set1[,c("class",cpredictors)]
  AllADNISets[[n]] <- AllADNISets[[n]][complete.cases(AllADNISets[[n]]),]
  AllADNISets[[n]]$class <- as.factor(AllADNISets[[n]]$class)
  print(nrow(AllADNISets[[n]]))
  print(table(set1$DX_bl,set1$class))
  AllADNI_Model[[n]] <- filteredFit(class ~ .,
                                    AllADNISets[[n]],
                                    fitmethod=e1071::svm,
                                    filtermethod.control=list(pvalue=0.1,limit=150))
  print(length(AllADNI_Model[[n]]$selectedfeatures))
}

pred <- predict(AllADNI_Model[[1]],ADNITEST)

for (n in 2:resamplingNumber)
{
  pred <- pred+predict(AllADNI_Model[[n]],ADNITEST)
}
pred <- pred/resamplingNumber

pb <- predictionStats_ordinal(cbind(ADNITEST$class,as.integer(pred+0.5)),plotname = "ADNI Classificaiton")

pb$class95ci
  
table(ADNITEST$class,as.integer(pred+0.5))

```